In [1]:
import pandas as pd
import numpy as np
# for visaluatztion
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
In [2]:
data = pd.read_csv("sentimentdataset.csv")
In [3]:
data.head()
Out[3]:
| Unnamed: 0.1 | Unnamed: 0 | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15.0 | 30.0 | USA | 2023 | 1 | 15 | 12 | |
| 1 | 1 | 1 | Traffic was terrible this morning. ... | Negative | 2023-01-15 08:45:00 | CommuterX | #Traffic #Morning | 5.0 | 10.0 | Canada | 2023 | 1 | 15 | 8 | |
| 2 | 2 | 2 | Just finished an amazing workout! 💪 ... | Positive | 2023-01-15 15:45:00 | FitnessFan | #Fitness #Workout | 20.0 | 40.0 | USA | 2023 | 1 | 15 | 15 | |
| 3 | 3 | 3 | Excited about the upcoming weekend getaway! ... | Positive | 2023-01-15 18:20:00 | AdventureX | #Travel #Adventure | 8.0 | 15.0 | UK | 2023 | 1 | 15 | 18 | |
| 4 | 4 | 4 | Trying out a new recipe for dinner tonight. ... | Neutral | 2023-01-15 19:55:00 | ChefCook | #Cooking #Food | 12.0 | 25.0 | Australia | 2023 | 1 | 15 | 19 |
In [4]:
data.shape
Out[4]:
(732, 15)
In [5]:
data.dtypes
Out[5]:
Unnamed: 0.1 int64 Unnamed: 0 int64 Text object Sentiment object Timestamp object User object Platform object Hashtags object Retweets float64 Likes float64 Country object Year int64 Month int64 Day int64 Hour int64 dtype: object
In [6]:
data.columns
Out[6]:
Index(['Unnamed: 0.1', 'Unnamed: 0', 'Text', 'Sentiment', 'Timestamp', 'User',
'Platform', 'Hashtags', 'Retweets', 'Likes', 'Country', 'Year', 'Month',
'Day', 'Hour'],
dtype='object')
In [7]:
data
Out[7]:
| Unnamed: 0.1 | Unnamed: 0 | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15.0 | 30.0 | USA | 2023 | 1 | 15 | 12 | |
| 1 | 1 | 1 | Traffic was terrible this morning. ... | Negative | 2023-01-15 08:45:00 | CommuterX | #Traffic #Morning | 5.0 | 10.0 | Canada | 2023 | 1 | 15 | 8 | |
| 2 | 2 | 2 | Just finished an amazing workout! 💪 ... | Positive | 2023-01-15 15:45:00 | FitnessFan | #Fitness #Workout | 20.0 | 40.0 | USA | 2023 | 1 | 15 | 15 | |
| 3 | 3 | 3 | Excited about the upcoming weekend getaway! ... | Positive | 2023-01-15 18:20:00 | AdventureX | #Travel #Adventure | 8.0 | 15.0 | UK | 2023 | 1 | 15 | 18 | |
| 4 | 4 | 4 | Trying out a new recipe for dinner tonight. ... | Neutral | 2023-01-15 19:55:00 | ChefCook | #Cooking #Food | 12.0 | 25.0 | Australia | 2023 | 1 | 15 | 19 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 727 | 728 | 732 | Collaborating on a science project that receiv... | Happy | 2017-08-18 18:20:00 | ScienceProjectSuccessHighSchool | #ScienceFairWinner #HighSchoolScience | 20.0 | 39.0 | UK | 2017 | 8 | 18 | 18 | |
| 728 | 729 | 733 | Attending a surprise birthday party organized ... | Happy | 2018-06-22 14:15:00 | BirthdayPartyJoyHighSchool | #SurpriseCelebration #HighSchoolFriendship | 25.0 | 48.0 | USA | 2018 | 6 | 22 | 14 | |
| 729 | 730 | 734 | Successfully fundraising for a school charity ... | Happy | 2019-04-05 17:30:00 | CharityFundraisingTriumphHighSchool | #CommunityGiving #HighSchoolPhilanthropy | 22.0 | 42.0 | Canada | 2019 | 4 | 5 | 17 | |
| 730 | 731 | 735 | Participating in a multicultural festival, cel... | Happy | 2020-02-29 20:45:00 | MulticulturalFestivalJoyHighSchool | #CulturalCelebration #HighSchoolUnity | 21.0 | 43.0 | UK | 2020 | 2 | 29 | 20 | |
| 731 | 732 | 736 | Organizing a virtual talent show during challe... | Happy | 2020-11-15 15:15:00 | VirtualTalentShowSuccessHighSchool | #VirtualEntertainment #HighSchoolPositivity | 24.0 | 47.0 | USA | 2020 | 11 | 15 | 15 |
732 rows × 15 columns
In [8]:
data[['Platform']]
Out[8]:
| Platform | |
|---|---|
| 0 | |
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| ... | ... |
| 727 | |
| 728 | |
| 729 | |
| 730 | |
| 731 |
732 rows × 1 columns
In [9]:
data[['Timestamp']]
Out[9]:
| Timestamp | |
|---|---|
| 0 | 2023-01-15 12:30:00 |
| 1 | 2023-01-15 08:45:00 |
| 2 | 2023-01-15 15:45:00 |
| 3 | 2023-01-15 18:20:00 |
| 4 | 2023-01-15 19:55:00 |
| ... | ... |
| 727 | 2017-08-18 18:20:00 |
| 728 | 2018-06-22 14:15:00 |
| 729 | 2019-04-05 17:30:00 |
| 730 | 2020-02-29 20:45:00 |
| 731 | 2020-11-15 15:15:00 |
732 rows × 1 columns
In [10]:
data['Sentiment'].unique()
Out[10]:
array([' Positive ', ' Negative ', ' Neutral ', ' Anger ',
' Fear ', ' Sadness ', ' Disgust ',
' Happiness ', ' Joy ', ' Love ',
' Amusement ', ' Enjoyment ', ' Admiration ',
' Affection ', ' Awe ', ' Disappointed ',
' Surprise ', ' Acceptance ', ' Adoration ',
' Anticipation ', ' Bitter ', ' Calmness ',
' Confusion ', ' Excitement ', ' Kind ',
' Pride ', ' Shame ', ' Confusion ', ' Excitement ',
' Shame ', ' Elation ', ' Euphoria ', ' Contentment ',
' Serenity ', ' Gratitude ', ' Hope ',
' Empowerment ', ' Compassion ', ' Tenderness ',
' Arousal ', ' Enthusiasm ', ' Fulfillment ',
' Reverence ', ' Compassion', ' Fulfillment ', ' Reverence ',
' Elation ', ' Despair ', ' Grief ',
' Loneliness ', ' Jealousy ', ' Resentment ',
' Frustration ', ' Boredom ', ' Anxiety ',
' Intimidation ', ' Helplessness ', ' Envy ',
' Regret ', ' Disgust ', ' Despair ',
' Loneliness ', ' Frustration ', ' Anxiety ', ' Intimidation ',
' Helplessness ', ' Jealousy ', ' Curiosity ',
' Indifference ', ' Confusion ', ' Numbness ',
' Melancholy ', ' Nostalgia ', ' Ambivalence ',
' Acceptance ', ' Determination ', ' Serenity ',
' Numbness ', ' Zest ', ' Contentment ', ' Hopeful ', ' Proud ',
' Grateful ', ' Empathetic ', ' Compassionate ', ' Playful ',
' Free-spirited ', ' Inspired ', ' Confident ', ' Serenity ',
' Curiosity ', ' Ambivalence ', ' Despair ', ' Bitterness ',
' Yearning ', ' Fearful ', ' Apprehensive ', ' Overwhelmed ',
' Jealous ', ' Devastated ', ' Frustrated ', ' Envious ',
' Dismissive ', ' Awe ', ' Determination ',
' Nostalgia ', ' Thrill ', ' Calmness ',
' Overwhelmed ', ' Gratitude ', ' Bittersweet ',
' Curiosity ', ' Admiration ', ' Overjoyed ',
' Inspiration ', ' Motivation ', ' Amusement ',
' Contemplation ', ' JoyfulReunion ', ' Excitement ',
' Satisfaction ', ' Blessed ', ' Anticipation ',
' Reflection ', ' Nostalgia ', ' Appreciation ',
' Confidence ', ' Surprise ', ' Accomplishment ',
' Wonderment ', ' Optimism ', ' Pride ',
' Happiness ', ' Curiosity ', ' Enchantment ',
' Intrigue ', ' PlayfulJoy ', ' Mindfulness ',
' DreamChaser ', ' Elegance ', ' Whimsy ', ' Pensive ',
' Thrill ', ' Harmony ', ' Creativity ', ' Radiance ',
' Wonder ', ' Rejuvenation ', ' Inspiration ',
' Coziness ', ' Gratitude ', ' Adventure ', ' Euphoria ',
' Awe ', ' Melodic ', ' FestiveJoy ',
' InnerJourney ', ' Freedom ', ' Dazzle ',
' Adrenaline ', ' Harmony ', ' ArtisticBurst ',
' Radiance ', ' Wonder ', ' Inspiration ',
' CulinaryOdyssey ', ' Euphoria ', ' Curiosity ',
' Resilience ', ' Immersion ', ' Nostalgia ', ' Spark ',
' Gratitude ', ' Marvel ', ' Serenity ',
' Heartbreak ', ' Loneliness ', ' Grief ',
' Despair ', ' Betrayal ', ' Suffering ',
' EmotionalStorm ', ' Regret ', ' Isolation ',
' Disappointment ', ' LostLove ', ' Melancholy ', ' Exhaustion ',
' Sorrow ', ' Darkness ', ' Desperation ', ' Ruins ',
' Desolation ', ' Regret ', ' Grief ', ' Heartbreak ',
' Betrayal ', ' Resilience ', ' Sorrow ', ' Loss ', ' Heartache ',
' Solitude ', ' Joy ', ' Happiness ', ' Enthusiasm ',
' Gratitude ', ' Positivity ', ' Kindness ', ' Friendship ',
' Love ', ' Surprise ', ' Success ', ' Thrill ', ' Reflection ',
' Enchantment ', ' Exploration ', ' Awe ', ' Amazement ',
' Romance ', ' Captivation ', ' Wonder ', ' Tranquility ',
' Grandeur ', ' Emotion ', ' Energy ', ' Celebration ', ' Charm ',
' Ecstasy ', ' Hope ', ' Creativity ', ' Colorful ', ' Pride ',
' Hypnotic ', ' Connection ', ' Iconic ', ' Euphoria ',
' Journey ', ' Engagement ', ' Touched ', ' Suspense ',
' Satisfaction ', ' Admiration ', ' Triumph ', ' Heartwarming ',
' Obstacle ', ' Sympathy ', ' Pressure ', ' Renewed Effort ',
' Miscalculation ', ' Challenge ', ' Solace ', ' Breakthrough ',
' Harmony ', ' Joy in Baking ', ' Envisioning History ',
' Imagination ', ' Vibrancy ', ' Mesmerizing ',
' Culinary Adventure ', ' Winter Magic ', ' Thrilling Journey ',
" Nature's Beauty ", ' Celestial Wonder ',
' Creative Inspiration ', ' Runway Creativity ',
" Ocean's Freedom ", ' Whispers of the Past ', ' Boredom ',
' Indifference ', ' Disgust ', ' Relief ', ' Positive ',
' Embarrassed ', ' Mischievous ', ' Sad ', ' Hate ', ' Bad ',
' Neutral ', ' Happy '], dtype=object)
In [11]:
data[['Sentiment']]
Out[11]:
| Sentiment | |
|---|---|
| 0 | Positive |
| 1 | Negative |
| 2 | Positive |
| 3 | Positive |
| 4 | Neutral |
| ... | ... |
| 727 | Happy |
| 728 | Happy |
| 729 | Happy |
| 730 | Happy |
| 731 | Happy |
732 rows × 1 columns
In [12]:
data[['Text']]
Out[12]:
| Text | |
|---|---|
| 0 | Enjoying a beautiful day at the park! ... |
| 1 | Traffic was terrible this morning. ... |
| 2 | Just finished an amazing workout! 💪 ... |
| 3 | Excited about the upcoming weekend getaway! ... |
| 4 | Trying out a new recipe for dinner tonight. ... |
| ... | ... |
| 727 | Collaborating on a science project that receiv... |
| 728 | Attending a surprise birthday party organized ... |
| 729 | Successfully fundraising for a school charity ... |
| 730 | Participating in a multicultural festival, cel... |
| 731 | Organizing a virtual talent show during challe... |
732 rows × 1 columns
code starts¶
In [14]:
data.shape
Out[14]:
(732, 15)
In [15]:
data.drop(columns='Unnamed: 0.1',inplace=True)
In [16]:
data.rename(columns={'Unnamed: 0':'Id'},inplace=True)
In [17]:
data.head()
Out[17]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15.0 | 30.0 | USA | 2023 | 1 | 15 | 12 | |
| 1 | 1 | Traffic was terrible this morning. ... | Negative | 2023-01-15 08:45:00 | CommuterX | #Traffic #Morning | 5.0 | 10.0 | Canada | 2023 | 1 | 15 | 8 | |
| 2 | 2 | Just finished an amazing workout! 💪 ... | Positive | 2023-01-15 15:45:00 | FitnessFan | #Fitness #Workout | 20.0 | 40.0 | USA | 2023 | 1 | 15 | 15 | |
| 3 | 3 | Excited about the upcoming weekend getaway! ... | Positive | 2023-01-15 18:20:00 | AdventureX | #Travel #Adventure | 8.0 | 15.0 | UK | 2023 | 1 | 15 | 18 | |
| 4 | 4 | Trying out a new recipe for dinner tonight. ... | Neutral | 2023-01-15 19:55:00 | ChefCook | #Cooking #Food | 12.0 | 25.0 | Australia | 2023 | 1 | 15 | 19 |
In [18]:
data.isnull().sum()
Out[18]:
Id 0 Text 0 Sentiment 0 Timestamp 0 User 0 Platform 0 Hashtags 0 Retweets 0 Likes 0 Country 0 Year 0 Month 0 Day 0 Hour 0 dtype: int64
In [19]:
data.dtypes
Out[19]:
Id int64 Text object Sentiment object Timestamp object User object Platform object Hashtags object Retweets float64 Likes float64 Country object Year int64 Month int64 Day int64 Hour int64 dtype: object
In [20]:
data['Retweets']=data['Retweets'].astype(int)
In [21]:
data['Retweets'].dtypes
Out[21]:
dtype('int32')
In [22]:
data[['Retweets']]
Out[22]:
| Retweets | |
|---|---|
| 0 | 15 |
| 1 | 5 |
| 2 | 20 |
| 3 | 8 |
| 4 | 12 |
| ... | ... |
| 727 | 20 |
| 728 | 25 |
| 729 | 22 |
| 730 | 21 |
| 731 | 24 |
732 rows × 1 columns
In [23]:
data['Likes']=data['Likes'].astype(int)
In [24]:
data['Likes'].dtype
Out[24]:
dtype('int32')
In [25]:
data[['Likes']]
Out[25]:
| Likes | |
|---|---|
| 0 | 30 |
| 1 | 10 |
| 2 | 40 |
| 3 | 15 |
| 4 | 25 |
| ... | ... |
| 727 | 39 |
| 728 | 48 |
| 729 | 42 |
| 730 | 43 |
| 731 | 47 |
732 rows × 1 columns
In [26]:
data.dtypes
Out[26]:
Id int64 Text object Sentiment object Timestamp object User object Platform object Hashtags object Retweets int32 Likes int32 Country object Year int64 Month int64 Day int64 Hour int64 dtype: object
In [27]:
data.head(1)
Out[27]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15 | 30 | USA | 2023 | 1 | 15 | 12 |
Timestap column¶
In [29]:
data['Timestamp'] = pd.to_datetime(data['Timestamp'])
data['Day'] = data['Timestamp'].dt.day
data['Month'] = data['Timestamp'].dt.month
data['Year'] = data['Timestamp'].dt.year
In [30]:
data.head(3)
Out[30]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15 | 30 | USA | 2023 | 1 | 15 | 12 | |
| 1 | 1 | Traffic was terrible this morning. ... | Negative | 2023-01-15 08:45:00 | CommuterX | #Traffic #Morning | 5 | 10 | Canada | 2023 | 1 | 15 | 8 | |
| 2 | 2 | Just finished an amazing workout! 💪 ... | Positive | 2023-01-15 15:45:00 | FitnessFan | #Fitness #Workout | 20 | 40 | USA | 2023 | 1 | 15 | 15 |
In [31]:
data['Sentiment'].value_counts().nlargest(10).plot(kind='bar')
plt.title('Top 10 Sentiments based on Text')
plt.xlabel('Sentiment')
plt.ylabel('Count')
plt.show()
In [32]:
data.columns
Out[32]:
Index(['Id', 'Text', 'Sentiment', 'Timestamp', 'User', 'Platform', 'Hashtags',
'Retweets', 'Likes', 'Country', 'Year', 'Month', 'Day', 'Hour'],
dtype='object')
In [33]:
data['Platform'].value_counts()
Out[33]:
Platform Instagram 258 Facebook 231 Twitter 128 Twitter 115 Name: count, dtype: int64
In [34]:
data['Country'].value_counts()
Out[34]:
Country
USA 59
USA 55
UK 49
Canada 44
Australia 41
..
Netherlands 1
USA 1
Germany 1
France 1
USA 1
Name: count, Length: 115, dtype: int64
In [35]:
data['Platform'].value_counts().plot(kind='pie', autopct='%1.1f%%')
plt.title('Percentages of Platforms')
plt.legend()
plt.show()
In [36]:
data[['Country']]
Out[36]:
| Country | |
|---|---|
| 0 | USA |
| 1 | Canada |
| 2 | USA |
| 3 | UK |
| 4 | Australia |
| ... | ... |
| 727 | UK |
| 728 | USA |
| 729 | Canada |
| 730 | UK |
| 731 | USA |
732 rows × 1 columns
In [37]:
data['Country'].unique()
Out[37]:
array([' USA ', ' Canada ', ' USA ', ' UK ',
' Australia ', ' India ', ' USA ', 'USA', ' Canada ',
' USA ', ' USA ', ' Canada ', ' UK ', ' India ',
' Canada ', ' UK ', ' India ', ' UK ', ' UK ',
' USA ', ' Canada ', ' USA ', ' India ',
' Australia ', ' UK ', ' Canada ',
' Australia ', ' Australia ', ' UK ', ' USA ',
' India ', ' UK ', ' USA ', ' UK ',
' Canada ', ' India ', ' Canada ',
' India ', ' Australia ', ' Canada ',
' India ', ' USA ', ' USA ',
' Canada ', ' UK ',
' India ', ' Australia ',
' France ', ' Brazil ',
' Japan ', ' Greece ',
' India ', ' Brazil ', ' France ',
' Japan ', ' Canada ', ' Australia ',
' UK ', ' USA ', ' Greece ',
' Germany ', ' Sweden ', ' Italy ',
' Netherlands ', ' South Africa ', ' Spain ',
' Portugal ', ' Switzerland ', ' Austria ',
' Belgium ', ' Denmark ', ' Czech Republic ',
' Canada ', ' France ', ' UK ',
' India ', ' Australia ', ' Japan ',
' Brazil ', ' Germany ', ' Australia ',
' France ', ' Italy ', ' Japan ',
' Spain ', ' Netherlands ', ' South Africa ',
' Germany ', ' USA ', ' France ', ' Brazil ',
' Germany ', ' Brazil ', ' France ', ' Italy ', ' Japan ',
' Spain ', ' Netherlands ', ' Greece ', ' Jordan ', ' Peru ',
' Maldives ', ' China ', ' Cambodia ', ' Switzerland ', ' Norway ',
' Colombia ', ' Ireland ', ' Jamaica ', ' Kenya ', ' Scotland ',
' Thailand ', ' USA', ' Canada', ' UK'], dtype=object)
In [38]:
data['Country'].value_counts().nlargest(10).plot(kind='bar')
plt.title('Top 10 Country')
plt.legend()
plt.show()
In [39]:
data['Hashtags']
Out[39]:
0 #Nature #Park
1 #Traffic #Morning
2 #Fitness #Workout
3 #Travel #Adventure
4 #Cooking #Food
...
727 #ScienceFairWinner #HighSchoolScience
728 #SurpriseCelebration #HighSchoolFriendship
729 #CommunityGiving #HighSchoolPhilanthropy
730 #CulturalCelebration #HighSchoolUnity
731 #VirtualEntertainment #HighSchoolPositivity
Name: Hashtags, Length: 732, dtype: object
In [40]:
data['Hashtags'].value_counts().nlargest(10).plot(kind='bar')
plt.title('Top 10 Hashtags')
plt.xlabel('Hashtags')
plt.ylabel('Count')
plt.legend()
plt.show()
In [41]:
# Describe data nurimucal
data.describe()
Out[41]:
| Id | Timestamp | Retweets | Likes | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|
| count | 732.000000 | 732 | 732.000000 | 732.000000 | 732.000000 | 732.000000 | 732.000000 | 732.000000 |
| mean | 369.740437 | 2020-12-08 21:37:38.196721408 | 21.508197 | 42.901639 | 2020.471311 | 6.122951 | 15.497268 | 15.521858 |
| min | 0.000000 | 2010-05-15 15:30:00 | 5.000000 | 10.000000 | 2010.000000 | 1.000000 | 1.000000 | 0.000000 |
| 25% | 185.750000 | 2019-03-08 17:27:30 | 17.750000 | 34.750000 | 2019.000000 | 3.000000 | 9.000000 | 13.000000 |
| 50% | 370.500000 | 2021-09-20 14:30:00 | 22.000000 | 43.000000 | 2021.000000 | 6.000000 | 15.000000 | 16.000000 |
| 75% | 553.250000 | 2023-02-26 11:48:45 | 25.000000 | 50.000000 | 2023.000000 | 9.000000 | 22.000000 | 19.000000 |
| max | 736.000000 | 2023-10-22 20:45:00 | 40.000000 | 80.000000 | 2023.000000 | 12.000000 | 31.000000 | 23.000000 |
| std | 212.428936 | NaN | 7.061286 | 14.089848 | 2.802285 | 3.411763 | 8.474553 | 4.113414 |
In [42]:
data.head(1)
Out[42]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15 | 30 | USA | 2023 | 1 | 15 | 12 |
Top 10 hashtags retweeted¶
In [44]:
H_R=data.groupby('Hashtags')['Retweets'].max().nlargest(10).sort_values(ascending=False).plot(kind='bar')
plt.title('Top 10 hashtags retweeted')
plt.xlabel('Hashtags')
plt.ylabel('count')
plt.show()
Which platform is top! liked by users¶
In [46]:
top_likes_platform = data.groupby('Platform')['Likes'].sum().nlargest(10)
top_likes_platform.plot(kind='bar')
plt.title('Top Platforms by Total Likes')
plt.xlabel('Platform')
plt.ylabel('Total Likes')
plt.show()
What is the most country liked posts¶
In [48]:
top_country_likes=data.groupby('Country')['Likes'].sum().nlargest(10)
top_country_likes.plot(kind='bar')
plt.title('Top country likes')
plt.xlabel('Country')
plt.ylabel('count')
plt.show()
EDA¶
In [50]:
import nltk # Natural Language Toolkit
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer #to remove suffixes
import re # regular expression
import string #to find occurences of a pattern, such as word or phrase, text
from nltk.stem import PorterStemmer
from nltk import tokenize
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
In [51]:
import nltk
nltk.download('stopwords') # commonly used words helps to processing..
[nltk_data] Downloading package stopwords to [nltk_data] C:\Users\hari6\AppData\Roaming\nltk_data... [nltk_data] Unzipping corpora\stopwords.zip.
Out[51]:
True
In [52]:
nltk.download('punkt') #sentence tokenizer
[nltk_data] Downloading package punkt to [nltk_data] C:\Users\hari6\AppData\Roaming\nltk_data... [nltk_data] Package punkt is already up-to-date!
Out[52]:
True
In [53]:
stemmer = PorterStemmer()
stop_words = set(stopwords.words('english'))
def clean(text):
text = str(text).lower()
text = re.sub('\[.*?\]', '', text) #replace match substring with a new string
text = re.sub('https?://\S+|www\.\S+', '', text)
text = re.sub(r'\s+', ' ', text.strip())
text = re.sub('<.*?>+', '', text)
text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
text = re.sub('\n', '', text)
text = re.sub('\w*\d\w*', '', text)
text = re.sub(r'[^\x00-\x7F]+', '', text)
text = " ".join(text.split())
tokens = word_tokenize(text)
cleaned_tokens = [stemmer.stem(token) for token in tokens if token.lower() not in stop_words]
cleaned_text = ' '.join(cleaned_tokens)
return cleaned_text
data["Clean_Text"] = data["Text"].apply(clean)
<>:6: SyntaxWarning: invalid escape sequence '\['
<>:7: SyntaxWarning: invalid escape sequence '\S'
<>:12: SyntaxWarning: invalid escape sequence '\w'
<>:6: SyntaxWarning: invalid escape sequence '\['
<>:7: SyntaxWarning: invalid escape sequence '\S'
<>:12: SyntaxWarning: invalid escape sequence '\w'
C:\Users\hari6\AppData\Local\Temp\ipykernel_10092\275143958.py:6: SyntaxWarning: invalid escape sequence '\['
text = re.sub('\[.*?\]', '', text) #replace match substring with a new string
C:\Users\hari6\AppData\Local\Temp\ipykernel_10092\275143958.py:7: SyntaxWarning: invalid escape sequence '\S'
text = re.sub('https?://\S+|www\.\S+', '', text)
C:\Users\hari6\AppData\Local\Temp\ipykernel_10092\275143958.py:12: SyntaxWarning: invalid escape sequence '\w'
text = re.sub('\w*\d\w*', '', text)
In [54]:
data1=data.copy()
In [55]:
data.head(1)
Out[55]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | Clean_Text | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15 | 30 | USA | 2023 | 1 | 15 | 12 | enjoy beauti day park |
In [56]:
data1.head(1)
Out[56]:
| Id | Text | Sentiment | Timestamp | User | Platform | Hashtags | Retweets | Likes | Country | Year | Month | Day | Hour | Clean_Text | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Enjoying a beautiful day at the park! ... | Positive | 2023-01-15 12:30:00 | User123 | #Nature #Park | 15 | 30 | USA | 2023 | 1 | 15 | 12 | enjoy beauti day park |
In [57]:
!pip install vaderSentiment
# VADER( Valence Aware Dictionary for Sentiment Reasoning)
# is an NLTK module that provides sentiment scores based on the words used.
Requirement already satisfied: vaderSentiment in d:\anaconda\lib\site-packages (3.3.2) Requirement already satisfied: requests in d:\anaconda\lib\site-packages (from vaderSentiment) (2.32.2) Requirement already satisfied: charset-normalizer<4,>=2 in d:\anaconda\lib\site-packages (from requests->vaderSentiment) (2.0.4) Requirement already satisfied: idna<4,>=2.5 in d:\anaconda\lib\site-packages (from requests->vaderSentiment) (3.7) Requirement already satisfied: urllib3<3,>=1.21.1 in d:\anaconda\lib\site-packages (from requests->vaderSentiment) (2.2.2) Requirement already satisfied: certifi>=2017.4.17 in d:\anaconda\lib\site-packages (from requests->vaderSentiment) (2024.7.4)
In [58]:
from nltk.sentiment import SentimentIntensityAnalyzer
In [59]:
analyzer = SentimentIntensityAnalyzer() #Sentiment score
vader_lexicon - Is anis a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from.¶
In [61]:
nltk.download('vader_lexicon')
[nltk_data] Downloading package vader_lexicon to [nltk_data] C:\Users\hari6\AppData\Roaming\nltk_data... [nltk_data] Package vader_lexicon is already up-to-date!
Out[61]:
True
In [62]:
data1['Vader_Score'] = data1['Clean_Text'].apply(lambda text: analyzer.polarity_scores(text)['compound'])
data1['Sentiment'] = data1['Vader_Score'].apply(lambda score: 'positive' if score >= 0.05 else ('negative' if score <= -0.05 else 'neutral'))
print(data1[['Clean_Text', 'Vader_Score', 'Sentiment']].head())
Clean_Text Vader_Score Sentiment 0 enjoy beauti day park 0.4939 positive 1 traffic terribl morn 0.0000 neutral 2 finish amaz workout 0.0000 neutral 3 excit upcom weekend getaway 0.0000 neutral 4 tri new recip dinner tonight 0.0000 neutral
In [63]:
colors = ['#66b3ff', '#99ff99', '#ffcc99']
In [64]:
explode = (0.1, 0, 0)
sentiment_counts = data1.groupby("Sentiment").size()
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(
x=sentiment_counts,
labels=sentiment_counts.index,
autopct=lambda p: f'{p:.2f}%\n({int(p*sum(sentiment_counts)/100)})',
wedgeprops=dict(width=0.7),
textprops=dict(size=10, color="r"),
pctdistance=0.7,
colors=colors,
explode=explode,
shadow=True)
center_circle = plt.Circle((0, 0), 0.6, color='white', fc='white', linewidth=1.25)
fig.gca().add_artist(center_circle)
ax.text(0, 0, 'Sentiment\nDistribution', ha='center', va='center', fontsize=14, fontweight='bold', color='#333333')
ax.legend(sentiment_counts.index, title="Sentiment", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
ax.axis('equal')
plt.show()
common words used in this dataset¶
In [66]:
from collections import Counter #We can count the frequency of the words in the file using the counter function
In [67]:
data1['temp_list'] = data1['Clean_Text'].apply(lambda x: str(x).split())
top_words = Counter([item for sublist in data1['temp_list'] for item in sublist])
top_words_df = pd.DataFrame(top_words.most_common(20), columns=['Common_words', 'count'])
top_words_df.style.background_gradient(cmap='Blues')
Out[67]:
| Common_words | count | |
|---|---|---|
| 0 | new | 43 |
| 1 | life | 37 |
| 2 | challeng | 34 |
| 3 | joy | 31 |
| 4 | danc | 30 |
| 5 | day | 29 |
| 6 | feel | 29 |
| 7 | like | 28 |
| 8 | dream | 28 |
| 9 | moment | 27 |
| 10 | emot | 27 |
| 11 | friend | 26 |
| 12 | heart | 26 |
| 13 | explor | 25 |
| 14 | echo | 25 |
| 15 | beauti | 24 |
| 16 | laughter | 24 |
| 17 | embrac | 24 |
| 18 | night | 23 |
| 19 | hope | 23 |
import plotly.express as px¶
In [69]:
import plotly.express as px
In [70]:
data1['temp_list'] = data1['Clean_Text'].apply(lambda x: str(x).split())
top_words = Counter([item for sublist in data1['temp_list'] for item in sublist])
top_words_df = pd.DataFrame(top_words.most_common(20), columns=['Common_words', 'count'])
fig = px.bar(top_words_df,
x="count",
y="Common_words",
title='Common Words in Text Data',
orientation='h',
width=700,
height=700,
color='Common_words')
fig.show()
Positive Common Words¶
In [72]:
top = Counter([item for sublist in data1[data1['Sentiment'] == 'positive']['temp_list'] for item in sublist])
temp_positive = pd.DataFrame(top.most_common(10), columns=['Common_words', 'count'])
temp_positive.style.background_gradient(cmap='Greens')
Out[72]:
| Common_words | count | |
|---|---|---|
| 0 | joy | 30 |
| 1 | friend | 24 |
| 2 | laughter | 24 |
| 3 | new | 21 |
| 4 | challeng | 20 |
| 5 | life | 20 |
| 6 | hope | 20 |
| 7 | dream | 20 |
| 8 | embrac | 19 |
| 9 | like | 19 |
In [73]:
pip install wordcloud
Requirement already satisfied: wordcloud in d:\anaconda\lib\site-packages (1.9.3) Requirement already satisfied: numpy>=1.6.1 in d:\anaconda\lib\site-packages (from wordcloud) (1.26.4) Requirement already satisfied: pillow in d:\anaconda\lib\site-packages (from wordcloud) (10.3.0) Requirement already satisfied: matplotlib in d:\anaconda\lib\site-packages (from wordcloud) (3.8.4) Requirement already satisfied: contourpy>=1.0.1 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (1.2.0) Requirement already satisfied: cycler>=0.10 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (0.11.0) Requirement already satisfied: fonttools>=4.22.0 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (4.51.0) Requirement already satisfied: kiwisolver>=1.3.1 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (1.4.4) Requirement already satisfied: packaging>=20.0 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (23.2) Requirement already satisfied: pyparsing>=2.3.1 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (3.0.9) Requirement already satisfied: python-dateutil>=2.7 in d:\anaconda\lib\site-packages (from matplotlib->wordcloud) (2.9.0.post0) Requirement already satisfied: six>=1.5 in d:\anaconda\lib\site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0) Note: you may need to restart the kernel to use updated packages.
In [74]:
from wordcloud import WordCloud # Get insight into the most popular concepts or to reveal sentiment.
# It provides instant analysis & visualization of word data and feedback
In [75]:
words = ' '.join([item for sublist in data1[data1['Sentiment'] == 'positive']['temp_list'] for item in sublist])
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(words)
plt.figure(figsize=(10, 8))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
Neutral Common Words¶
In [77]:
top = Counter([item for sublist in data1[data1['Sentiment'] == 'neutral']['temp_list'] for item in sublist])
temp_positive = pd.DataFrame(top.most_common(10), columns=['Common_words', 'count'])
temp_positive.style.background_gradient(cmap='Blues')
Out[77]:
| Common_words | count | |
|---|---|---|
| 0 | new | 22 |
| 1 | explor | 13 |
| 2 | excit | 12 |
| 3 | life | 12 |
| 4 | beauti | 12 |
| 5 | night | 12 |
| 6 | danc | 12 |
| 7 | attend | 11 |
| 8 | seren | 11 |
| 9 | feel | 10 |
In [78]:
words = ' '.join([item for sublist in data1[data1['Sentiment'] == 'neutral']['temp_list'] for item in sublist])
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(words)
plt.figure(figsize=(10, 8))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
Negative Common Words¶
In [80]:
top = Counter([item for sublist in data1[data1['Sentiment'] == 'negative']['temp_list'] for item in sublist])
temp_positive = pd.DataFrame(top.most_common(10), columns=['Common_words', 'count'])
temp_positive.style.background_gradient(cmap='Reds')
Out[80]:
| Common_words | count | |
|---|---|---|
| 0 | despair | 14 |
| 1 | lost | 14 |
| 2 | emot | 13 |
| 3 | feel | 11 |
| 4 | bitter | 10 |
| 5 | storm | 10 |
| 6 | day | 9 |
| 7 | like | 9 |
| 8 | grief | 8 |
| 9 | heart | 8 |
In [81]:
words = ' '.join([item for sublist in data1[data1['Sentiment'] == 'negative']['temp_list'] for item in sublist])
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(words)
plt.figure(figsize=(10, 8))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
Data Preparation¶
In [83]:
data2 = data1.copy()
In [84]:
from sklearn.feature_extraction.text import TfidfVectorizer #term frequency inverse document frequency
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import confusion_matrix
Split Data¶
In [86]:
X = data2['Clean_Text'].values
y = data2['Sentiment'].values
In [87]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Modeling¶
In [89]:
vectorizer = TfidfVectorizer(max_features=5000)
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
Logistic Classifier¶
In [91]:
logistic_classifier = LogisticRegression(max_iter=50, random_state=42)
logistic_classifier.fit(X_train, y_train)
Out[91]:
LogisticRegression(max_iter=50, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=50, random_state=42)
In [92]:
y_pred_logistic = logistic_classifier.predict(X_test)
accuracy_logistic = accuracy_score(y_test, y_pred_logistic)
classification_rep_logistic = classification_report(y_test, y_pred_logistic)
In [93]:
print("Logistic Regression Results:")
print(f"Accuracy: {accuracy_logistic}")
print("Classification Report:\n", classification_rep_logistic)
Logistic Regression Results:
Accuracy: 0.6326530612244898
Classification Report:
precision recall f1-score support
negative 0.89 0.50 0.64 32
neutral 0.80 0.36 0.50 55
positive 0.55 0.95 0.70 60
accuracy 0.63 147
macro avg 0.75 0.60 0.61 147
weighted avg 0.72 0.63 0.61 147
Random Forest Classifier¶
In [95]:
random_forest_classifier = RandomForestClassifier(random_state=42)
random_forest_classifier.fit(X_train, y_train)
Out[95]:
RandomForestClassifier(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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RandomForestClassifier(random_state=42)
In [96]:
y_pred_rf = random_forest_classifier.predict(X_test)
accuracy_rf = accuracy_score(y_test, y_pred_rf)
classification_rep_rf = classification_report(y_test, y_pred_rf)
In [97]:
print("\nRandom Forest Results:")
print(f"Accuracy: {accuracy_rf}")
print("Classification Report:\n", classification_rep_rf)
Random Forest Results:
Accuracy: 0.6530612244897959
Classification Report:
precision recall f1-score support
negative 0.86 0.59 0.70 32
neutral 0.77 0.44 0.56 55
positive 0.56 0.88 0.69 60
accuracy 0.65 147
macro avg 0.73 0.64 0.65 147
weighted avg 0.71 0.65 0.64 147
Passive Aggressive Classifier¶
In [99]:
pac_classifier = PassiveAggressiveClassifier(max_iter=50, random_state=42)
pac_classifier.fit(X_train, y_train)
Out[99]:
PassiveAggressiveClassifier(max_iter=50, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
PassiveAggressiveClassifier(max_iter=50, random_state=42)
In [100]:
y_pred = pac_classifier.predict(X_test)
accuracy_test = accuracy_score(y_test, y_pred)
classification_rep_test = classification_report(y_test, y_pred)
In [101]:
print("Test Set Results:")
print(f"Accuracy: {accuracy_test}")
print("Classification Report:\n", classification_rep_test)
Test Set Results:
Accuracy: 0.7142857142857143
Classification Report:
precision recall f1-score support
negative 0.79 0.81 0.80 32
neutral 0.73 0.55 0.62 55
positive 0.67 0.82 0.74 60
accuracy 0.71 147
macro avg 0.73 0.72 0.72 147
weighted avg 0.72 0.71 0.71 147
SVM Classifier¶
In [103]:
svm_classifier = SVC(random_state=42)
svm_classifier.fit(X_train, y_train)
Out[103]:
SVC(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC(random_state=42)
In [104]:
y_pred_svm = svm_classifier.predict(X_test)
accuracy_svm = accuracy_score(y_test, y_pred_svm)
classification_rep_svm = classification_report(y_test, y_pred_svm)
In [108]:
print("Support Vector Machine Results:")
print(f"Accuracy: {accuracy_svm}")
print("Classification Report:\n", classification_rep_svm)
Support Vector Machine Results:
Accuracy: 0.5986394557823129
Classification Report:
precision recall f1-score support
negative 1.00 0.38 0.55 32
neutral 0.94 0.31 0.47 55
positive 0.50 0.98 0.67 60
accuracy 0.60 147
macro avg 0.82 0.56 0.56 147
weighted avg 0.78 0.60 0.57 147
Best Modeling Is : Passive Aggressive Classifier¶
In [112]:
pip install powerbiclient
Collecting powerbiclientNote: you may need to restart the kernel to use updated packages.
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Preparing metadata (setup.py): started
Preparing metadata (setup.py): finished with status 'done'
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Collecting py4j==0.10.9.7 (from pyspark->powerbiclient)
Downloading py4j-0.10.9.7-py2.py3-none-any.whl.metadata (1.5 kB)
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Collecting fqdn (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.6->ipywidgets>=7.0.0->powerbiclient)
Downloading fqdn-1.5.1-py3-none-any.whl.metadata (1.4 kB)
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Building wheels for collected packages: pyspark
Building wheel for pyspark (setup.py): started
Building wheel for pyspark (setup.py): finished with status 'done'
Created wheel for pyspark: filename=pyspark-3.5.5-py2.py3-none-any.whl size=317747901 sha256=64453c99a625055eb7b7a563ebcf760d48cc332f8971b26f93d433830bd55b6c
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Successfully built pyspark
Installing collected packages: py4j, webcolors, uri-template, pyspark, fqdn, isoduration, msal, jupyter-ui-poll, powerbiclient
Successfully installed fqdn-1.5.1 isoduration-20.11.0 jupyter-ui-poll-1.0.0 msal-1.32.0 powerbiclient-3.1.1 py4j-0.10.9.7 pyspark-3.5.5 uri-template-1.3.0 webcolors-24.11.1
In [116]:
from powerbiclient import Report, models
In [123]:
from powerbiclient.authentication import DeviceCodeLoginAuthentication
device_auth = DeviceCodeLoginAuthentication()
Performing device flow authentication. Please follow the instructions below. To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code NN4WGJX8D to authenticate.
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[123], line 2 1 from powerbiclient.authentication import DeviceCodeLoginAuthentication ----> 2 device_auth = DeviceCodeLoginAuthentication() File D:\anaconda\Lib\site-packages\powerbiclient\authentication.py:78, in DeviceCodeLoginAuthentication.__init__(self, tenant_id) 76 super().__init__() 77 CheckGlobalAuth() ---> 78 self._acquire_token_device_code(tenant_id) File D:\anaconda\Lib\site-packages\powerbiclient\authentication.py:110, in DeviceCodeLoginAuthentication._acquire_token_device_code(self, tenant_id) 108 else: 109 error_message = f"Authentication failed. Try again.\nDetails: {result.get('error_description')}" --> 110 raise RuntimeError(error_message) RuntimeError: Authentication failed. Try again. Details: AADSTS70016: OAuth 2.0 device flow error. Authorization is pending. Continue polling. Trace ID: 7531ef1e-23b2-48c3-a88d-dfb280f03600 Correlation ID: 5dbfc74d-81f1-42b6-9f4c-c1133c61d300 Timestamp: 2025-03-13 14:18:24Z
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